摘要
在分析破碎机典型故障原理及其基本特征的基础上,利用小波包分析将振动信号分解到不同波段,采用能量归一化处理后形成特征向量输入bp神经网络,通过网络训练后用于实际故障识别,结果证明该方案具有较高的正确率,可有效识别和预警破碎机各类故障。
Based on analysis of the principle of typical faults and the basic charateristics of coal crusher,the vibration signal generated by crusher in operation can first be decomposed into different wave bands by analysis and then inputted into bp neural network in forms of eigenvectors after energy normalization process.The network-trained neural network can be used for identification of any actual fault of the crusher.As evidenced by test result,the technology is high in accuracy and capable of effectively identifying and early warning of any kinds of faults of crusher in speration.
作者
蔡先锋
CAI Xianfeng(Tangshan Research Institute Co.Ltd.,China Coal Technology&Engineering Group,Tangshan 063012,China;Hebei Province Coal Preparation Engineering&Technology Research Center,Tangshan 063012,China)
出处
《选煤技术》
CAS
2019年第6期102-105,109,共5页
Coal Preparation Technology
关键词
破碎机
故障
小波包
神经网络
crusher
fault
wavelet packet
neural network
